Compression of color digital images using vector quantization in product codes

There is a growing interest in the use of vector quantization for coding digital images. A key issue to be resolved is how to achieve perceptually pleasing results while limiting encoding complexity to tolerable levels. In this paper, product codes are described which improve the quality of the encoded edges and textures for a given level of complexity. These product codes separate the mean and orientation information from each source vector and encode this information independently to allow the residual to be vector quantized more accurately. The color image coder also reduces the required bit rate by taking advantage of spectral redundancy. Experimental results indicate that an improvement of almost 1.4 dB in SNR can be achieved over a Discrete Cosine Transform block coder of comparable complexity, with negligible computational complexity added by the product structure.